🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here.
Tutorial
Advanced
⏱ 45 min read
© Gate of AI 2026-06-03
Build a production-grade multi-agent workflow using LangGraph v1.2. Use state-based orchestration to manage autonomous reasoning loops securely.
Prerequisites
- Python 3.10+
- LangChain v1.3.4+ and LangGraph v1.2.4+
- OpenAI API Key (GPT-4o)
- Understanding of Pydantic and TypedDict for state management
Installation
pip install langchain==1.3.4 langgraph==1.2.4 langchain-openai
Step 1: Define the State Schema
In modern agentic workflows, "Memory" is replaced by an explicit State Schema. This allows the graph to pass data between nodes with type safety.
from typing import TypedDict, Annotated
import operator
from langchain_core.messages import BaseMessage
class AgentState(TypedDict):
# Annotate as 'list' to append new messages instead of overwriting
messages: Annotated[list[BaseMessage], operator.add]
task_goal: str
generated_topology: str
Step 2: Orchestrate with LangGraph
We replace the legacy Agent class with Nodes and Edges. This allows for "Time-Travel Debugging" and human-in-the-loop checkpoints.
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o", temperature=0.2)
Define Node Logic
def understand_task(state: AgentState):
# ... logic to parse natural language ...
return {"task_goal": "Optimized Ethylene Cracking"}
def generate_topology(state: AgentState):
# ... logic to output process structure ...
return {"generated_topology": "C2H4 -> C2H2 + H2"}
Build the Graph
workflow = StateGraph(AgentState)
workflow.add_node("understand", understand_task)
workflow.add_node("topology", generate_topology)
workflow.set_entry_point("understand")
workflow.add_edge("understand", "topology")
workflow.add_edge("topology", END)
app = workflow.compile()
Testing the Workflow
To run the production-grade agent, we invoke the graph with an initial state.
result = app.invoke({"messages": ["Design an ethylene cracking process"]})
print(result["generated_topology"])
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